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arXiv · 2610.05365

Poisson Autoregression on a Large Network with a Stochastic Block Model Structure

Mahmoud Khabou·Joanna Marks·Joshua Corneck·Edward A. K. Cohen
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Abstract

We consider multivariate Poisson autoregressive processes evolving on random networks generated by a Stochastic Block Model. This framework extends existing network Poisson autoregressions by incorporating latent community structure and allowing for non-Markovian dependence, both excitatory and inhibitory. We establish a mean-field approximation and prove that the finite system converges to its limit at the optimal rate $N^{-1/2}$ in the quenched setting with overwhelming probability, both on average and in uniform norm. Our analysis yields quantitative error bounds that are explicit in the time horizon and, under suitable stability conditions, remain polynomial or uniform in time, thereby avoiding the exponential growth typically associated with Grönwall-type arguments. As a consequence, the mean-field approximation is shown to hold uniformly over any time horizon. We further demonstrate how the approximation can be exploited to reduce the computational complexity of parametric inference from $\mathcal{O}(N^2T^2)$ to $\mathcal{O}(NT^2)$. The results provide a theoretical foundation for the analysis and inference of count-valued processes on large random networks with community structure.

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Mahmoud Khabou, Joanna Marks, Joshua Corneck, Edward A. K. Cohen. 2026-10-04. Poisson Autoregression on a Large Network with a Stochastic Block Model Structure. https://arxiv.org/abs/2610.05365

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